KICO2Opt

With annual CO₂ emissions of around 58 million tons, metal processing is one of the most energy-intensive industries in Germany. The KICO2Opt project aims to significantly reduce CO₂ emissions in machining through the targeted use of artificial intelligence (AI) under real production conditions in a medium-sized company.

A central component is the creation of digital twins of machines and processes that communicate via standardised interfaces such as the Asset Administration Shell (AAS). These digital images enable seamless recording and visualisation of all CO₂-relevant process parameters, from material data to energy consumption and tool wear. Based on this, AI forecasting models are developed that enable precise emission predictions per batch and the identification of reduction potential.

KICO2Opt takes a holistic approach − from the early estimation of emissions based on CAD design data and AI-supported production planning through to CO₂-optimised setup planning, waste assessment and machine usage. The modular solution can also be transferred to other manufacturing processes in the future. The AI results are visualised via an interactive CO₂ cockpit and explained by an LLM-supported decision-making system, transparent, comprehensible and understandable for employees without AI knowledge.

The aim of KICO2Opt is to use data-driven intelligence to make metal processing more sustainable, competitive and climate-friendly, while leading the way in a practical, scalable and exemplary manner for the industry.

Further information on the project can be found here: https://kico2opt.hs-furtwangen.de/